I've been experimenting with parallel GA's for a few years now.
Doing something that requires parallel evaluation of cost
functions coupled with a parallel GA is a good application for
small clusters. It allows you to consider a wider range of
applications since you have more horsepower. It also allows
you to consider more members in the population, which should
help with finding the optimal point.
Another really good GA application is multi-objective optimization.
In these types of problems you are trying to find the pareto-optimal
front, which sometimes means that you need a large population to
define the front. Also, as you add objectives, you will need a larger
population. In either case, this means more horsepower, more
collective memory, and perhaps the application of parallel
techniques to improve the search techniques.
You know I should keep a list of what people are posting to
summarize this discussion. I'll probably have to do it at some
point for the BOB column in CW. :)
Thanks!
Jeff
> Here's an intriguing possibility for use on a cheap cluster:
>http://simulationresearch.lbl.gov/GO/>> Genopt is a generic optimization program. It invokes an external
> program to evaluate the cost function, and implements a variety of
> ways to do the optimization. Some of these might very amenable to EP
> execution on a cluster. (Particle Swarm and Pattern Search for instance).
>> Some sort of GA might also be a good fit to a cluster.
>> Genopt is fairly non-optimized. It spits out a text file to your
> evaluation/simulation program, then it reads the text file generated
> by the evalutor to extract the cost. It's more of an optmizer wrapper
> around some other tool.
>>> James Lux, P.E.
> Spacecraft Radio Frequency Subsystems Group
> Flight Communications Systems Section
> Jet Propulsion Laboratory, Mail Stop 161-213
> 4800 Oak Grove Drive
> Pasadena CA 91109
> tel: (818)354-2075
> fax: (818)393-6875